Manufacturing Platform Engineer - Software Development, Annapurna Labs

Amazon

You are given a string moves of length n consisting only of characters 'L', 'R', and '_'. The string represents your movement on a number line starting from the origin 0. In the ith move, you can choose one of the following directions: move to the left if moves[i] = 'L' or moves[i] = '_' move to the right if moves[i] = 'R' or moves[i] = '_' Return the *distance from the origin* of the *furthest* point you can get to after n moves. Example 1: Input: moves = L_RL__R Output: 3 Explanation: The furthest point we can reach from the origin 0 is point -3 through the following sequence of moves LLRLLLR. Example 2: Input: moves = R__LL Output: 5 Explanation: The furthest point we can reach from the origin 0 is point -5 through the following sequence of moves LRLLLLL. Example 3: Input: moves = _ Output: 7 Explanation: The furthest point we can reach from the origin 0 is point 7 through the following sequence of moves RRRRRRR.

Amazon

Let's explore a scenario involving data transformation. Imagine you're receiving a stream of customer data from various sources. This data includes customer IDs, names, email addresses, and purchase histories. However, the data is inconsistent: some sources use different formats for dates, some have missing fields, and others use abbreviations for states. Your task is to design a robust and efficient system to clean and transform this data into a consistent format suitable for analysis. Specifically: Data Cleaning: How would you handle missing values, inconsistent date formats (e.g., MM/DD/YYYY vs. YYYY-MM-DD), and variations in state abbreviations (e.g., CA vs. California)? Provide code examples (Python is preferred) demonstrating how you would address these issues. Data Transformation: How would you transform the data to ensure consistency? For example, you might need to convert all dates to a standard format, expand state abbreviations to their full names, and ensure all customer IDs are in a uniform format. Scalability: How would you design the system to handle a large volume of data (e.g., millions of records per day)? Consider the technologies and architectures you would use to ensure scalability and performance. Think about potential bottlenecks and how to address them. Error Handling: Describe how you would implement error handling and logging to identify and address data quality issues. What metrics would you track to monitor the quality of the transformed data? For instance, suppose you receive the following data snippets: Source 1: {customer_id: 123, name: Alice, email: alice@example.com, purchase_date: 01/01/2023, state: CA} Source 2: {CustomerID: 456, Name: Bob, Email: bob@example.com, PurchaseDate: 2023-01-01, State: California} How would your system handle these variations and transform them into a unified format like this: {customer_id: 123, name: Alice, email: alice@example.com, purchase_date: 2023-01-01, state: California}

Amazon

Tell me about a time you disagreed with a colleague. What was your approach to resolving the disagreement, and what was the outcome? How did you ensure that your interaction remained professional and respectful, even with differing viewpoints? For example, imagine you're working on a new feature for an e-commerce platform. You believe that a simplified checkout process will lead to higher conversion rates, while your colleague argues that adding more security steps is crucial, even if it adds friction for the user. Describe the situation, your reasoning, your colleague's reasoning, and the steps you took to reach a resolution that benefited the project and maintained a positive working relationship. What did you learn from this experience?